{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T21:01:38Z","timestamp":1779915698764,"version":"3.53.1"},"reference-count":27,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019082","name":"Shanghai Aerospace Science and Technology Innovation Fund","doi-asserted-by":"publisher","award":["SAST2019-048"],"award-info":[{"award-number":["SAST2019-048"]}],"id":[{"id":"10.13039\/501100019082","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019082","name":"Shanghai Aerospace Science and Technology Innovation Fund","doi-asserted-by":"publisher","award":["BNR2019TD01022"],"award-info":[{"award-number":["BNR2019TD01022"]}],"id":[{"id":"10.13039\/501100019082","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019082","name":"Shanghai Aerospace Science and Technology Innovation Fund","doi-asserted-by":"publisher","award":["61563049"],"award-info":[{"award-number":["61563049"]}],"id":[{"id":"10.13039\/501100019082","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Cross-Media Intelligent Technology Project of Beijing National Research Center for Information Science and Technology (BNRist)","award":["SAST2019-048"],"award-info":[{"award-number":["SAST2019-048"]}]},{"name":"Cross-Media Intelligent Technology Project of Beijing National Research Center for Information Science and Technology (BNRist)","award":["BNR2019TD01022"],"award-info":[{"award-number":["BNR2019TD01022"]}]},{"name":"Cross-Media Intelligent Technology Project of Beijing National Research Center for Information Science and Technology (BNRist)","award":["61563049"],"award-info":[{"award-number":["61563049"]}]},{"name":"National Natural Science Foundation of China","award":["SAST2019-048"],"award-info":[{"award-number":["SAST2019-048"]}]},{"name":"National Natural Science Foundation of China","award":["BNR2019TD01022"],"award-info":[{"award-number":["BNR2019TD01022"]}]},{"name":"National Natural Science Foundation of China","award":["61563049"],"award-info":[{"award-number":["61563049"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aiming at a thorny issue, that conventional small target detection algorithm using local contrast method is not sensitive for residual background clutter, robustness of algorithms is not strong. A Gaussian fusion algorithm using multi-scale regional patch structure difference and Regional Brightness Level Measurement is proposed. Firstly, Regional Energy Cosine (REC) is constructed to measure the structural discrepancy among a small target with neighboring cells. At the same time, Regional Brightness Level Measurement (RBLM) is constructed utilizing the brightness difference characteristics between small target and background areas. Then, a brand new Gaussian fusion algorithm is proposed for the generated saliency map in multi-scale space to characterize the overall heterogeneity in original infrared small target and local neighborhood. Finally, a self-adapting separation algorithm is adopted with the objective to obtain a small target from background interference. This method is able to utmostly restrain background interference and enhance the target. Extensive qualitative and quantitative testing results display that the desired algorithm has remarkable performance in strengthening target region and restraining background interference compared with current algorithms.<\/jats:p>","DOI":"10.3390\/s22093277","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T02:14:39Z","timestamp":1650939279000},"page":"3277","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Infrared Small Target Detection Using Regional Feature Difference of Patch Image"],"prefix":"10.3390","volume":"22","author":[{"given":"Guofeng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"},{"name":"Institute of Information Science and Engineering, Changji Vocational and Technical College, Changji 831100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1785-4024","authenticated-orcid":false,"given":"Hongbing","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"},{"name":"Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2321-308X","authenticated-orcid":false,"given":"Askar","family":"Hamdulla","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.neucom.2020.08.065","article-title":"Infrared small target detection via self-regularized weighted sparse model","volume":"420","author":"Zhang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, X., Ye, P., and Xiao, G. 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